Train Navigator GPS Track Database Error Compensation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing train location systems face inaccuracies due to track irregularities and errors in track databases, leading to false alarms and reduced navigation accuracy, especially in environments with varying track conditions and maintenance-induced non-uniformities.
Innovation Solution
A stochastic model using a digital Kalman filter for Bayesian estimation of error variables, processing individual satellite GPS data, and incorporating track alignment compensation to correct for track database errors, allowing for accurate navigation and route determination even with limited satellite visibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If track database information is used for train navigation, then navigation functionality is provided, but navigation accuracy deteriorates due to track irregularities and database errors
Solution Approach 1:
The system implements a feedback mechanism where the estimated train position from GPS is continuously compared with the expected position from track database. The difference (position error) is fed back to the Kalman filter, which adjusts its estimates of track database errors and navigation parameters to minimize this error, thereby improving navigation accuracy despite database imperfections
Solution Approach 2:
The Kalman filter acts as an intermediary that reconciles conflicting information from GPS measurements and track database. It processes both data sources, estimates the underlying true position, and compensates for database errors, providing a more accurate position estimate than either source alone
2Measurement precision
If standard GPS processing is used, then position estimation is provided, but accuracy deteriorates in environments with limited satellite visibility
Solution Approach 1:
The system performs preliminary action by using the track database to predict expected train position and velocity before GPS measurement is processed. This prediction serves as a prior that constrains the GPS solution, enabling accurate position estimation even with limited satellite visibility by preventing unrealistic position estimates
Solution Approach 2:
The Kalman filter dynamically changes parameters (error variances, state estimates) based on the quality and quantity of available GPS measurements. When satellite visibility is limited, the filter adjusts its confidence in GPS measurements versus track database predictions, maintaining accuracy across varying satellite conditions
3Reliability
If track database errors are not compensated, then system complexity is reduced, but false alarms increase due to position errors
Solution Approach 1:
The Kalman filter serves multiple functions simultaneously: it estimates train position, estimates track database errors, and provides compensated position information for navigation. This multi-functionality reduces false alarms without requiring separate error compensation systems, managing complexity through unified processing
4Measurement precision
If highly accurate physical surveys are used to create track databases, then database accuracy is improved, but cost and time requirements increase
Solution Approach 1:
The system performs self-service by using operational GPS measurements combined with the Kalman filter to automatically estimate and compensate for track database errors in real-time. This eliminates the need for costly and time-consuming repeated physical surveys, as the system self-corrects for database inaccuracies during normal operation
Data Source
AI summary
The present invention provides a new set of algorithmic solutions to accommodate track inaccuracy information in track databases. Navigation and measurement aiding processes are defined by a stochastic mode relative to a moving rail frame defined so that it is aligned with the heading of the compensated track database at the current along track-position. Filtering generates long and short wavelength track alignment disturbances commensurate with track grade to compensate for track database errors; a stochastic error model is defined as the difference between the deterministic implementation and the actual stochastic processes Bayesian estimation of the error variables is implemented via a digital Kalman filter with the navigation, database, and measurement errors removed by subtracting the filter estimates.